Identification of Robust Correlations between EEG Connectivity Metrics and Intelligence Components
DOI:
https://doi.org/10.14529/cmse220402Keywords:
EEG, resting state, brain network connectivity, intelligence, graph measures, robustness, correlationAbstract
According to the “neural efficiency” hypothesis, intelligence indicators are associated with specific spatial features of the optimal functional activity of the brain. Since there are studies that do not confirm such a relationship, the study of the issue of its stability remains relevant. Therefore, the main task of the study is to find metrics of EEG connectivity at rest that are stably correlated with indicators of the verbal and visual-spatial components of intelligence. Pearson’s and Spearman’s correlation coefficients, polychoric correlation coefficient and their stable analogs calculated on the basis of truncation, the MCD method, and the sign method were chosen as potential measures of the relationship of the studied parameters. To assess the robustness to outliers, the “leave-one-out test” (LOOT) procedure was used, on the basis of which a weighted robust analog of the correlation coefficients was calculated. By the degree of deviation from its initial value, calculated for the entire sample, one can judge the sensitivity to outliers. It is shown that rank-based correlation coefficients using truncation are the most resistant to outliers. As a result, stable significant correlations were found between intelligence indicators and EEG connectivity at rest, indicating a potentially effective pre-tuning of functional neural networks with the combination of local and distantly distributed neural ensembles.References
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